M.M.A. Salama

Power quality detection and classification using wavelet-multiresolution signal decomposition

The wavelet transform is introduced as a powerful tool for monitoring power quality problems generated due to the dynamic performance of industrial plants. The paper presents a multiresolution signal decomposition technique as an efficient method in analyzing transient events. The multiresolution signal decomposition has the ability to detect and localize transient events and furthermore classify different power quality disturbances. It can also be used to distinguish among similar disturbances.

Optimal investment planning for distributed generation in a competitive electricity market

This paper proposes a new heuristic approach for distributed generation (DG) capacity investment planning from the perspective of a distribution company (disco). Optimal sizing and siting decisions for DG capacity is obtained through a cost-benefit analysis approach based on a new optimization model. The model aims to minimize the discos investment and operating costs as well as payment toward loss compensation. Bus-wise cost-benefit analysis is carried out on an hourly basis for different forecasted peak demand and market price scenarios. This approach arrives at the optimal feasible DG capacity investment plan under competitive electricity market auction as well as fixed bilateral contract scenarios. The proposed heuristic method helps alleviate the use of binary variables in the optimization model thus easing the computational burden substantially.

Classification of capacitor allocation techniques

The problem of capacitor allocation for loss reduction in electric distribution systems has been extensively researched over the past several decades. This paper describes the evolution of the research and provides an evaluation of the practicality and accuracy of the capacitor placement algorithms in the literature. The intent of this paper is not to provide a complete survey of all the literature in capacitor allocation, but to provide researchers and utility engineers further insight into the choices of available capacitor allocation techniques and their respective merits and shortcomings. Furthermore, this paper serves as a useful and practical guideline to assist in the implementation of an appropriate capacitor allocation technique.

A novel population initialization method for accelerating evolutionary algorithms

Population initialization is a crucial task in evolutionary algorithms because it can affect the convergence speed and also the quality of the final solution. If no information about the solution is available, then random initialization is the most commonly used method to generate candidate solutions (initial population). This paper proposes a novel initialization approach which employs opposition-based learning to generate initial population. The conducted experiments over a comprehensive set of benchmark functions demonstrate that replacing the random initialization with the opposition-based population initialization can accelerate convergence speed.

Opposition versus randomness in soft computing techniques

For many soft computing methods, we need to generate random numbers to use either as initial estimates or during the learning and search process. Recently, results for evolutionary algorithms, reinforcement learning and neural networks have been reported which indicate that the simultaneous consideration of randomness and opposition is more advantageous than pure randomness. This new scheme, called opposition-based learning, has the apparent effect of accelerating soft computing algorithms. This paper mathematically and also experimentally proves this advantage and, as an application, applies that to accelerate differential evolution (DE). By taking advantage of random numbers and their opposites, the optimization, search or learning process in many soft computing techniques can be accelerated when there is no a priori knowledge about the solution. The mathematical proofs and the results of conducted experiments confirm each other.

Trends in partial discharge pattern classification: a survey

Partial discharge (PD) detection, measurement and classification constitute an important tool for quality assessment of insulation systems utilized in HV power apparatus and cables. The patterns obtained with PD detectors contain characteristic features of the source/class of the respective partial discharge process involved. The recognition of the source from the data represents the classification stage. Usually, this stage consists of a two-step procedure, i.e., extraction of feature vector from the data followed by classification/recognition of the corresponding source. The various techniques available for achieving the foregoing task are examined and analyzed; while limited success has been achieved in the identification of simple PD sources, recognition and classification of complex PD patterns associated with practical insulating systems continue to pose appreciable difficulty.

Investigation of Methods for Reduction of Power Fluctuations Generated From Large Grid-Connected Photovoltaic Systems

Photovoltaic (PV) systems are presently allowed to inject into the grid all the power they can generate. However, in the near future, utilities are expected to impose additional regulations and restrictions on the power being injected by large centralized PV systems because of their possible adverse impacts. One of the main issues associated with large PV systems is the fluctuation of their output power. These fluctuations can negatively impact the performance of the electric networks to which these systems are connected, especially if the penetration levels of these systems are high. Moreover, the fluctuations in the power of PV systems make it difficult to predict their output, and thus, to consider them when scheduling the generating units in the network. The main objective of this paper is to investigate some methods that can be used to reduce the fluctuations in the power generated from a large customer-owned PV system, in the order of megawatts. This paper focuses on three methods: 1) the use of battery storage systems; 2) the use of dump loads; and 3) curtailment of the generated power by operating the power-conditioning unit of the PV system below the maximum power point. The emphasis in the analysis presented in this paper is on investigating the impacts of implementing these methods on the economical benefits that the PV system owner gains. To estimate the maximum revenues gained by the system owner, an linear programming optimization problem is formulated and solved. Moreover, the effect of varying different parameters of the problem is investigated through sensitivity analysis.

Capacitor allocation by approximate reasoning: fuzzy capacitor placement

The problem of capacitor allocation in electric distribution systems involves maximizing energy and peak power (demand) loss reductions by means of capacitor installations. This paper presents a novel approach using approximate reasoning to determine suitable candidate nodes in a distribution system for capacitor placement. Voltages and power loss reduction indices of distribution system nodes are modeled by fuzzy membership functions. A fuzzy expert system (FES) containing a set of heuristic rules is then used to determine the capacitor placement suitability of each node in the distribution system. Capacitors are placed on the nodes with the highest suitability. Simulation results show the advantages of this approach over previous capacitor placement algorithms.

Adequacy assessment of distributed generation systems using Monte Carlo Simulation

This paper presents a Monte Carlo-based method for the adequacy assessment of distributed generation systems. The state duration sampling approach is employed in this paper to model the operating histories of the installed distributed generators. A general procedure to assess the ability of the system power capacity to meet the total demand is presented and implemented in a typical case study where several distributed generation units are running in parallel within a sample distribution system and the system margins and the average amount of unsupplied loads are estimated using Monte Carlo simulation. The results obtained are presented and a new perspective to the power management of distribution systems is discussed.

A survey of the state of the art in distribution system reconfiguration for system loss reduction

Abstract A survey of publications in the area of distribution system reconfiguration for loss reduction is presented. The prevailing world social, political, and economic climates dictate that every effort be made to render the generation, transmission, and distribution of electricity as efficient as possible. Through the use of existing tie and sectionalizing switches, reconfiguration of the distribution system represents an attractive method of loss reduction as it can be implemented at minimal cost to the utility. Techniques of reconfiguration, ranging from the fundamental work of Merlin and Bach to the current state of the art, are outlined.